Land degradation/Description

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Two approaches to assess land degradation in IMAGE 3.0
Flowchart Land degradation. See also the Input/Output Table on the introduction page.

Model description of Land degradation

Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; Oldeman et al. (1991)). IMAGE assesses soil erosion by water (Hootsmans et al., 2001) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

A. Risk of soil erosion caused by water

Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; Oldeman et al. (1991)). IMAGE assesses soil erosion by water (Hootsmans et al., 2001) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

  • terrain erodibility index: terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the WISE database (Batjes, 1997).
  • rainfall erosivity index: this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period (Harris et al., 2013). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component Atmospheric composition and climate. The number of wet days per month is assumed to be constant over time.
  • land-use/land-cover index: this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

T = (Ia+ SE)/2
Ep = (T+R)/2
WES = Ep•V


Ia = relief index (-)
SE = soil erodibility index (-)
T = terrain erodibility index (-)
R = rainfall erosivity index (-)
Ep = water erosion susceptibility index (-)
V = land-use/land-cover index (-)
WES = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by Oldeman et al. (1991) shows maximum correspondence with use of the classification in the table below. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

Classification of the Water Erosion Sensitivity Index
Water Erosion Sensitivity Index GLASOD soil degradation caused by water erosion
< 0.15 no/low
0.15 - 0.30 moderate
0.30 - 0.45 high
> 0.45 very high

B. Human-induced soil changes

Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter (SOM) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) (Stoorvogel, 2014; Stoorvogel et al., 2017). The following soil properties are considered:

  • topsoil depth,
  • soil depth,
  • soil organic matter in the topsoil and subsoil , and
  • soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database (HWSD; (FAO et al., 2009) and the WISE soil profile database (Batjes, 2009). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [vls..vhs] in which vls corresponds to the 1st decile and vhs to the 9th decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [p1,p2… p5]. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:

  • 1.0 for cropland,
  • 0.5 for mosaics of cropland and pasture or natural vegetation,
  • 0.3 for pasture, and
  • 0.0 for natural vegetation;

Land cover is characterised by a remotely sensed NDVI map.

The soil property v at location x with soil s is estimated as:

Land degradation formula 1

with wx being a weight w∈ [0..1] that determines where v is in the range [vls..vhs ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as:

Land degradation formula 3

The weight wpx for landscape property p is calculated as:

Land degradation formula 2

In which cpv is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of cpv indicates whether there is a positive or negative relationship between the landscape property and the soil property.


Land degradation formula 3

and all the w∈ [0..1] then all values in the range [vls..vhs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Component Carbon cycle and natural vegetation ) and GLOFRIS (Component Flood risks), as alternative input to assess the consequences of historical or future land degradation.